scholarly journals A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

2014 ◽  
Vol 14 (3) ◽  
pp. 5494-5499
Author(s):  
Sreeparna Banerjee ◽  
Diptoneel Kayal
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Visual Inspection ◽  
Fundus Image ◽  
Analysis Program ◽  
Hard Exudates ◽  
Lighting Conditions ◽  
Retinal Fundus Image ◽  
Inner Diameter ◽  
Retinal Fundus

Diabetic retinopathy is considered to be one of the major causes of blindness among diabetes mellitus patients. Due to diabetic retinopathy blood vessels of retina gets damaged and fat, lipoprotein substances gets leaked out of the damaged blood vessels and are deposited in the intra retinal space. These substances are viewed as yellowish or whitish in color and are termed as exudates. They are the most important visible sign of the presence of diabetic retinopathy. Exudates are of two types, (a) hard exudates and (b) soft exudates. If the disease is not detected in early stages then it may lead to complete loss of vision to the diabetes patients. Detection of exudates is extremely difficult to detect by visual inspection due to small inner diameter of retina and inadequate lighting conditions. An efficient image analysis program can detect the presence effectively. In this paper we have proposed an automatic method for detection of hard exudates. The proposed method exhibits a sensitivity of 97.60% and specificity of 93% and accuracy of 95.70%.

Feature extraction from retinal fundus image for early detection of diabetic retinopathy

2013 ◽  
Author(s):  
Syna Sreng ◽  
Jun-Ichi Takada ◽  
Noppadol Maneerat ◽  
Don Isarakorn ◽  
Ruttikorn Varakulsiripunth ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus

scholarly journals Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis

2016 ◽  
Vol 10 (2) ◽  
pp. 254-261 ◽  
Author(s):  
Malavika Bhaskaranand ◽  
Chaithanya Ramachandra ◽  
Sandeep Bhat ◽  
Jorge Cuadros ◽  
Muneeswar Gupta Nittala ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Fundus Image ◽  
Diabetic Retinopathy Screening ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Retinopathy Screening

scholarly journals Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures

2018 ◽  
Vol 7 (4.33) ◽  
pp. 110
Author(s):  
Ahmad Firdaus Ahmad Fadzil ◽  
Zaaba Ahmad ◽  
Noor Elaiza Abd Khalid ◽  
Shafaf Ibrahim
Keyword(s):  
Blood Vessels ◽  
Visual Information ◽  
Object Oriented ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.      

scholarly journals Diabetic Retinopathy Detection using Image Processing

2019 ◽  
Vol 8 (6S) ◽  
pp. 937-941
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Medical Image ◽  
Histogram Equalization ◽  
Fundus Image ◽  
Adaptive Histogram Equalization ◽  
Retinal Fundus Image ◽  
Equalization Algorithm ◽  
Processing Techniques ◽  
Retinal Fundus

The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB (R2010a) and it is widely used in image processing. This paper proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with open CV (Computer Vision) framework implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and 98% of Accuracy can be achieved in the detection of DR.

Diabetic Retinopathy Prediction Device System

2019 ◽  
Vol 16 (10) ◽  
pp. 4266-4270
Author(s):  
Meenu Garg ◽  
Sheifali Gupta ◽  
Rakesh Ahuja ◽  
Deepali Gupta
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Treatment Effect ◽  
Physiological Parameters ◽  
Early Prediction ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Image Capturing ◽  
The One ◽  
Retinal Fundus

The present study relates to diagnostic devices, and more specifically, to a diabetic retinopathy prediction device, system and method for early prediction of diabetic retinopathy with application of deep learning. The device includes an image capturing device, a memory coupled to processor. The image capturing device obtains a retinal fundus image from the user. The memory comprising executable instructions which upon execution by the processor configures the device to obtain physiological parameters of the user in real-time from the image capturing device, retrieve the obtained retinal fundus image and the one or more obtained physiological parameters and compare the one or more extracted features with at least one pre-stored feature in a database to generate at least a prediction result indicative of detection of the presence, the progression or the treatment effect of the disease in the user.

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